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AT2: Quantified Self Report
Assessment 2 general guidance
Assignment two has three parts:
1. AT2a is due week 5, and is a short online form (only available in the week before due
date)
2. AT2b is due week 9, via Canvas, and consists of (a) a draft of your final submission,
and (b) your feedback to your class colleagues via peer review
3. AT2c is your final submission, due in the UTS exam period
This structure ensures you’re on track for the assignment, and provides an opportunity for
you to resubmit your AT2 taking into account the feedback provided to make changes.
For AT2 you will collect, record, share, and analyse several types of data about yourself and
compare and contrast what you find in your analysis with an analysis of the same data from
the group. The following requirements apply to your data collection:
1. Two sources of data negotiated with your group for sharing:
a. Unstructured. One of which must be unstructured in nature (e.g. text,
comments, images, audio, etc. You might obtain this from social media, email,
slack, twitter, daily photos, etc.) - you may find the ‘what does facebook know
about you’ materials useful for this.
b. Your choice. The second source can be structured, drawing on one of the
many examples provided.
2. You are expected to individually collect one other data source of personal
interest to you. This data does not need to be shared across the groups, but should
be analysed by you in your report.
3. One external cohort-level dataset (this might be summary data): The idea of this
dataset is that you will have data from: (1) an individual, (2) a small group, and (3) a
larger cohort. You might, for example, draw on published summary level data (for
example, what is the average step count in Australia?...for who?), or publicly
available stepcount data.
You will negotiate and agree a processes for recording, sharing and storing the data being
collected as a group, in the on-campus briefing session for AT2. Your attendance at this
session will be crucial in getting off to a strong start with a minimum of disruption for this
major task.
Examples of data that you and your group could collect include: daily step counts; pulse
rates; time spent on activities each day (exercise, grooming, travelling, eating/cooking,
shopping, sleeping studying, etc.); sleep patterns; daily spending; number & length of
conversations each day; location tracking, and so on.
Guidance on the criteria
Criterion 1: Strength of justification for the method to obtain data from multiple sources, for
gaining insight into a chosen problem, including analysis of data quality issues in the
individual and group data. Consider, do you tell the reader:
● What data you’re collecting
● Why that data is interesting to collect
● How you’re collecting the data, justifying that method (evaluate the benefits/issues
with the app or method you choose, etc.)
● What is the quality of the data you have (missing data, issues in the data, etc.) and
implications
Criterion 2: Insightfulness in the analysis of the obtained data, including quality issues, to
draw conclusions in a professional and engaging manner. Consider, do you:
● Provide analysis, presenting both a range of visualizations/data summaries and
drawing conclusions from them?
● Contextualise your findings, noting your particular context (you know about this
data!), and limitations in the analysis?
● Use appropriate analysis (we discuss this in class)
Criterion 3 Insightfulness in identification, contextualisation and reflection on ethical,
privacy, and legal issues relevant to the collection, analysis, and use of one's own and
other's personal data: The AT2 is designed to give you experience in collecting and
working with personal data. It can be confronting at times, and you should consider issues of
privacy and ethics, with regard to both the specifics of what you did, and implications for data
science, connecting to legal and ethical frameworks. You could imagine what would happen
if a data science troll (malicious agent) gained access to your data. We want you to consider:
● What harm can be done? (i.e., what is the risk, and the likelihood of those risks
occurring?). You could consider this both in relation to insights from your own data,
and the implications of those insights over much larger datasets. Imagine that we
conducted your QS project on many more people, and now have millions of
datapoints on the variables you collected...what insights can be gained (by
individuals and companies?), what is the balance of concerns? What are the
legal/privacy implications (i.e., what laws or principles have been breached?) and
ethical ethical considerations? (i.e., what insight do ethical frameworks provide us to
navigate the issue)
● What strategies could be adopted to do differently? Again, consider both your own
case (“We should have…”) and wider practice (“App policies should…”)
Criterion 4 Strength of connection between the individual experience of this QS
project to the practice of data science (and the preceding three criteria): When you’re
writing, think about both the specifics of your own analysis and insights, and what your work
tells us about the wider practice and implications of data science, drawing on sources to
contextualise and support your claims.
Criterion 5 Level of professionalism in the presentation appropriate to the discipline:
You can see specific guidance on this criterion in the subject outline. Remember, your
visualisations, and the way you develop your narrative are a part of professional
presentation. You should draw on external sources to support and contextualise your work
throughout. Be careful to emphasise interpretation and analysis over description and
narrative. So, don’t tell us about discussions you had and who said what (description), tell us
about the decisions you made, why, and their implications for the practice of data science
(analysis).
Scaffold for AT2
To support you in AT2, you should explore:
Timeline for AT2
To keep on track, here’s roughly where you should be at for each week:
1. Pre-work What Does Facebook Know About Me?
2. Choose group, establish communication and data sharing method, begin sharing
data
3. Be able to justify your approach “for the method to obtain data from multiple sources,
for gaining insight into a chosen problem, including analysis of data quality issues in
the individual and group data” (Criterion 1) - draft this section in the template
4. Ensure you have a shared dataset in preparation for Mystery Box formative task;
start to think about insights (criterion 2)
5. AT2a due, group status update, and your preliminary thoughts on analysis and
external (ideally scholarly) resources you’re drawing on
6. Continue thinking about insights you might gain, visualisations you can use, issues
(including ethical) with your data (criteria 2 and 3). Review sample assignments and
the AT2 template.
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7. Focus on issues with your data (including ethical) (criteria 1-3) and their implications
for the practice of data science (criterion 4)
8. Continue from week 7, with a particular focus on how comparing across the levels of
data (individual, group, cohort) provides insights. Ensure you have considered the
privacy and ethical issues throughout your report, and the implications of the project
for the practice of data science
9. Week 9, draft submission of AT2b.
10. Week 10, review colleague’s AT2b and continue work on your own final submission
11. Week 11, review colleague’s AT2b and continue work on your own final submission
12. Week 12 AT2b - feedback due. You should use that feedback to reflect on how to
improve for your final submission
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13. Final assessment period, AT2c due
How to write and submit AT2
Data scientists don’t just use Word and Powerpoint to write. They also write live reports, that
draw on real-time data to show visualisations alongside narratives. These can draw on
databases of data, to allow us to write text and do reproducible analysis of data for insights.
One of the key tools to do this is the ‘notebook’ file.
For AT2, we want you to use RStudio to write and submit your report. We know this will be
unfamiliar for many of you, but that’s ok. We’re not asking you to learn to code. We’ve
provided a template, and if you want, you can simply modify the example ‘markdown’ to
format your own report, and load visualisations that you’ve created in other tools (like Excel,
or Tableau). Some of you will want to go further, and that’s ok too! But remember to address
the assessment criteria - this isn’t an assignment where you have to demonstrate technical
coding skills.